What is Reinforcement Learning?
Reinforcement Learning can be explained by a few equations. However I assume that this is not what you are looking at since the explanation should be for someone having a non-STEM background. Not to say non-STEM folks are not able to understand math equations, but intuition comes easier with words and examples in my opinion.

Reinforcement Learning is about learning an optimal behavior by
repeatedly executing actions, observing the feedback from the
environment and adapting future actions based on that feedback.

Let's break down the last sentence by the concrete example of learning how to play chess:
Imagine you sit in front of a chess board, not knowing how to play. The optimal behavior you'd like to learn is what moves to execute in order to win the game. So you start learning the game by playing a few moves (actions) with some figures and observing what is happening on the board (environment) and identifying which moves bring you closer victory or give you a better position on the board (feedback). Therefore in future games you will prefer moves which gave you a positive outcome in previous games.
Admittedly this is a very slow process of learning if you don't have a teacher which helps you in the beginning and you would have to play a lot of games until your first victory. But this essentially how computers (and sometimes humans in some sense) learn to do certain things by Reinforcement Learning. Behaviors which lead to positive experiences are collected, memorized and thus reinforced.